Method and system for anomaly detection and network deployment based on quantitative assessment

1. A method comprising: receiving, by a device, network performance data pertaining to one or multiple network devices of a wireless network, wherein the network performance data includes performance indicator values that have n dimensions, network configuration data pertaining to the one or multiple network devices, and geographic information pertaining to the one or multiple network devices; encoding, by the device, the network performance data to m dimensions, wherein m dimensions is fewer than the n dimensions; generating, by the device based on the encoded network performance data and a generative model, latent space data that is representative of the network performance data; calculating, by the device, quantitative values, which pertain to one or more quantitative criteria, using the latent space data and decoded latent space data; storing, by the device, the quantitative values; determining, by the device, whether the encoding, the generating, the calculating, and the storing are to be performed again; performing again, by the device in response to determining that the performing is to be performed again, the encoding, the generating, the calculating, and the storing, wherein the performing again includes changing one or more parameters associated with the encoding, the generating, and the calculating; comparing, by the device in response to determining that the performing is not to be performed again, the quantitative values to threshold values; selecting, by the device based on the comparing, one or multiple instances of latent space data used during one or multiple corresponding iterations of the calculating; and generating, by the device based on the selecting, first data that indicates network performance for a geographic area of a wireless service area.

2. The method of claim 1 , wherein the geographic area includes at least one of one or multiple locations within which the one or multiple network devices reside or one or multiple locations that receive wireless service from the one or multiple network devices.

3. The method of claim 1 , wherein the geographic area includes at least one of one or multiple locations within which one or multiple candidate network devices are to be deployed or one or multiple locations where prospective wireless service is to be provided.

4. The method of claim 1 , wherein the quantitative criteria includes at least one of joint entropy, total correlation, divergence, or generalized Hausdorff distance.

5. The method of claim 1 , wherein the changing comprises: changing, by the device, the one or more parameters including one or more of a value of m, an optimizer type, an activation function, or hyperparameters of the generative model.

6. The method of claim 1 , wherein the one or multiple network devices include one or multiple wireless stations, the wireless network includes a radio access network, and the geographic information includes terrain information pertaining to a service area of a wireless service.

7. The method of claim 1 , wherein the performance indicator values include key performance indicator values, and wherein the generative model includes a generative adversarial network.

8. The method of claim 1 , further comprising: generating, by the device based on the selecting, second data that indicates a detected anomaly pertaining to at least one of the one or multiple network devices; and using the second data to remedy the detected anomaly.

9. A device comprising: a communication interface; a memory, wherein the memory stores instructions; and a processor, wherein the processor executes the instructions to: receive, via the communication interface, network performance data pertaining to one or multiple network devices of a wireless network, wherein the network performance data includes performance indicator values that have n dimensions, network configuration data pertaining to the one or multiple network devices, and geographic information pertaining to the one or multiple network devices; encode the network performance data to m dimensions, wherein m dimensions is fewer than the n dimensions; generate, based on the encoded network performance data and a generative model, latent space data that is representative of the network performance data; calculate quantitative values, which pertain to one or more quantitative criteria, using the latent space data and decoded latent space data; store the quantitative values; determine whether the encoding, the generating, the calculating, and the storing are to be performed again; perform again, in response to a determination that the performing is to be performed again, the encoding, the generating, the calculating, and the storing, wherein the performing again includes changing one or more parameters associated with the encoding, the generating, and the calculating; compare, in response to a determination that the performing is not to be performed again, the quantitative values to threshold values; select, based on the comparison, one or multiple instances of latent space data used during one or multiple corresponding iterations of the calculation; and generate, based on the selection, first data that indicates network performance for a geographic area of a wireless service area.

10. The device of claim 9 , wherein the geographic area includes at least one of one or multiple locations within which the one or multiple network devices reside or one or multiple locations that receive wireless service from the one or multiple network devices.

11. The device of claim 9 , wherein the geographic area includes at least one of one or multiple locations within which one or multiple candidate network devices are to be deployed or one or multiple locations where prospective wireless service is to be provided.

12. The device of claim 9 , wherein the quantitative criteria includes at least one of joint entropy, total correlation, divergence, or generalized Hausdorff distance.

13. The device of claim 9 , wherein, when changing, the processor further executes the instructions to: change the one or more parameters including one or more of a value of m, an optimizer type, an activation function, or hyperparameters of the generative model.

14. The device of claim 9 , wherein the one or multiple network devices include one or multiple wireless stations, the wireless network includes a radio access network, and the geographic information includes terrain information pertaining to a service area of a wireless service.

15. The device of claim 9 , wherein the performance indicator values include key performance indicator values, and wherein the generative model includes a generative adversarial network.

16. The device of claim 9 , wherein the processor further executes the instructions to: generate, based on the selection, second data that indicates a detected anomaly pertaining to at least one of the one or multiple network devices; and using the second data to remedy the detected anomaly.

17. A non-transitory, computer-readable storage medium storing instructions executable by a processor of a computational device, which when executed cause the computational device to: receive network performance data pertaining to one or multiple network devices of a wireless network, wherein the network performance data includes performance indicator values that have n dimensions, network configuration data pertaining to the one or multiple network devices, and geographic information pertaining to the one or multiple network devices; encode the network performance data to m dimensions, wherein m dimensions is fewer than the n dimensions; generate, based on the encoded network performance data and a generative model, latent space data that is representative of the network performance data; calculate quantitative values, which pertain to one or more quantitative criteria, using the latent space data and decoded latent space data; store the quantitative values; determine whether the encoding, the generating, the calculating, and the storing are to be performed again; perform again, in response to a determination that the performing is to be performed again, the encoding, the generating, the calculating, and the storing, wherein the performing again includes changing one or more parameters associated with the encoding, the generating, and the calculating; compare, in response to a determination that the performing is not to be performed again, the quantitative values to threshold values; select, based on the comparison, one or multiple instances of latent space data used during one or multiple corresponding iterations of the calculation; and generate, based on the selection, first data that indicates network performance for a geographic area of a wireless service area.

18. The non-transitory, computer-readable medium of claim 17 , wherein the geographic area includes at least one of one or multiple locations within which the one or multiple network devices reside or one or multiple locations that receive wireless service from the one or multiple network devices.

19. The non-transitory, computer-readable storage medium of claim 17 , wherein the geographic area includes at least one of one or multiple locations within which one or multiple candidate network devices are to be deployed or one or multiple locations where prospective wireless service is to be provided.

20. The non-transitory, computer-readable storage medium of claim 17 , wherein the one or multiple network devices include one or multiple wireless stations, the wireless network includes a radio access network, and the geographic information includes terrain information pertaining to a service area of a wireless service.